Learn how to make variations of bar plots and alternatives.
Data import from a .csv file using the readr package.
Scales with alternate color palettes.
Small multiples using facet_ functions.
A few fct_() functions for categorical variables using the forcats package.
Alternate coordinate systems including coord_polar and coord_fixed().
Dip our toes in the dplyr water: filter(), add_count(), and distinct().
Baboon Activities
We’re going to explore some real baboon activity data collected during the year 2000 from 64 adult female baboons in 5 social groups in the Amboseli basin, Kenya.
We first use the read_csv() function from the readr package. Much more on this later in the course when we talk about importing data to R.
Before executing this code, you must download the data file and save it on your computer. It might need to be modified, depending on what your folder is called.
# Read baboon activity data# MODIFY AS NEEDED!b_acts <-read_csv(here("assignments/week-04/data/baboon_acts_2000.csv"))
Rows: 40292 Columns: 11
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): month_of, sname, sex, activity
dbl (4): year_of, grp, min, age_sample
date (2): date, birth
time (1): stime
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
First, an annoyance to fix. The column “month_of” is an abbreviated month name, which R has interpreted as a character string. By default, it will be sorted alphabetically, but we want the months in their natural order. The following line reorders the months using a built-in set of ordered month abbreviations called month.abb.
Let’s look at the first 10 rows of data in a table using the kable function that you saw last week. We’ll use the head() function to get 10 rows.
kable(head(b_acts, n =10))
date
year_of
month_of
sname
birth
sex
grp
stime
min
age_sample
activity
2000-09-02
2000
Sep
HUM
1995-11-10
Female
1.1
10:12:00
2
4.813142
Walk
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
1
12.057495
Rest
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
2
12.057495
Rest
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
3
12.057495
Rest
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
4
12.057495
Walk
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
5
12.057495
Walk
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
6
12.057495
Rest
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
7
12.057495
Walk
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
8
12.057495
Walk
2000-01-07
2000
Jan
WEA
1987-12-17
Female
2.2
13:20:00
9
12.057495
Walk
Each row of data is an observation of a baboon activity behavior. In total, there are 40,292 observations of baboon activity behavior.
Pies
Let’s build on our in-class activities by making a pie chart out of this data (using the column activity). In addition, let’s use a scale_ function to map activity to a color scheme that is slightly less retina-scorching. In particular: scale_fill_brewer(palette = "Set1") uses a colorbrewer color palette called “Set1” for the fill aesthetic. These come with ggplot2. I also suggest using theme_void(), and setting color = "white" in geom_bar() so that the wedges are more clearly separable.
Pie template
ggplot(b_acts, aes(x ="", fill = activity)) +geom_bar(position ="fill", color ="white") +scale_fill_brewer(palette ="Set1") +coord_polar(theta ="y") +theme_void()
No need to write an answer, but can you figure out what color = "white" does here?
This will be our starting template for making pie charts below.
Problem 1: Small Multiples
One of the really nice things about ggplot2 is that it’s ridiculously easy to break apart your plot into small multiples. Let’s re-plot the baboon activity data in pies, this time separately by month. All we need to do is tack on a facet_wrap() line and provide it with the appropriate variable by which we want to break the data apart inside the vars() function.
Building on our starting plot, facet the plot by the variable month_of.
Problem 2: Small multiples with collapsed categories
These complex pies still have too many categories to be easily interpreted. This pie chart can be made (marginally) more acceptable by displaying only two categories. To do this, we need to collapse all but one category into a new category called “Other”. Let’s compare the proportion of time spent walking to all other categories in each month.
The collapsing can be done easily using the tidyverse package forcats. Yes, that is the actual name. It is used forcategorical data. Haha, get it?!!?! 🐱🐱🐱🐱
Uhhh… anyway, all forcats functions start with the prefix fct_. Here, fct_other() is exactly what we need, and we can use it “on the fly” inside our plotting code. See if you can figure out how to do this by reading the help file.
Hint: use it when the variable activity appears in the code: replace activity with fct_other(activity, <stuff here>).
Note that the legend title becomes unruly by calling fct_other() function within the aes() function. You can fix this by entering a legend title manually in the labs() function: labs(fill = "Behavior").
Bar charts
Leaving behind fraught the world of pies, we now turn our attention to bar charts.
This is our basic starting point for bars: NOT IDEAL
ggplot(b_acts, aes(x = month_of, fill = activity)) +geom_bar(color ="white") +scale_fill_brewer(palette ="Set1", direction =-1) +# Flip direction because I like having the red on the bottomlabs(x ="Month", y ="Number of Samples", fill ="Behavior")
Problem 3: Reordering the bars
We can improve this bar chart slightly by reordering the activities in order of their frequencies. For this, we can use another forcats function fct_reorder(), which as the name suggests reorders the category by some other variable. We first need to create the variable by which to order. Specifically, we want to count the number of rows with of each activity. dplyr has a convenient function called add_count() for this purpose.
How to use: add_count(<name of data>, <name of variable to count>). The count is added in a new column called “n”.
b_acts <- b_acts |>add_count(activity)
Now let’s use our code from the starting point to make another bar chart, but this time, with activity categories reordered by the frequency at which they occur.
Hint: like before, use it within the plotting code when the variable activity appears in the code: ... fill = fct_reorder(<stuff here>).
Problem 4: 100% Stacking
Still, It’s hard to make meaningful comparisons across months for two main reasons:
The categories start and end at different baselines, which makes them hard to compare. For example, were there more total Walk (blue) samples in February or September?
The different months have different total sample sizes, which makes the relative frequencies hard to interpret. For example, is Rest (green) a larger part of the activity budget in January, June, September, or November? Who knows.
Will changing the position help?
Modify your code to show the same data, but filling the entire space (i.e., using 100% stacking). Don’t forget to change the y-axis label!
Problem 5: Small multiples with bars
Another idea: instead of showing all the categories together, what about small multiples? Give this a try, faceting your data by activity. Try also adjusting the number of facet columns.
Problem 6: Hide the legend
New problems: our text along the x-axis became a bit squished, and the legend is now redundant. And actually, addressing one of these problems could solve the other. Let’s try suppressing the legend! Do this by using the guides() function. Read the help page (especially the examples) to try to figure out how to use it.
Alternatives to the regular bar chart
Our main interest here representing the activity budget, which is most commonly expressed in proportions rather than raw counts. What proportion of time did the baboons spend resting, feeding, socializing, etc.?
Some of the “better alternative” plots that we’re trying to work our way to (e.g., line charts) require that we summarize the data by actually calculating these proportions. As you will see, we can easily recreate the bar charts that we have already made from the summarized data using geom_col(), for which the default stat is "identity" rather than "count".
Here’s a bit of dplyr magic that may look cryptic now but, by the end of this course, you will be able to understand it. This produces our summarized data set:
# Reorder the activities using fct_reorder.# This way we don't have to do in in each plot going forwardb_acts <- b_acts |>mutate(activity =fct_reorder(activity, n))# Summarize by calculating proportionsb_acts_prop <- b_acts |>group_by(month_of) |>count(activity, name ="n") |>mutate(prop = n /sum(n)) |>ungroup() |>complete(month_of, activity, fill =list(prop =0, n =0))
Now we have a new summarized data set:
kable(head(b_acts_prop, n =10))
month_of
activity
n
prop
Jan
Be groomed by infant
7
0.0022103
Jan
Other social
5
0.0015788
Jan
Groom infant
61
0.0192611
Jan
Be groomed
109
0.0344174
Jan
Groom
138
0.0435744
Jan
Rest
841
0.2655510
Jan
Walk
913
0.2882854
Jan
Feed
1093
0.3451216
Feb
Be groomed by infant
0
0.0000000
Feb
Other social
3
0.0006890
Problem 7: Sanity check
Let’s verify that we get the SAME 100% stacking plot from Problem 4, but this time using geom_col() with the new data set called b_acts_prop. Think carefully about how you have to change your aes() mappings!
Problem 8: Line chart
Let’s use a completely different geom and show this data using a line chart. Try to recreate the plot from my solutions file as best as possible.
Hints:
In the aesthetic mappings, use group = activity to make the lines properly.
Lines and points use the color aesthetic rather than the fill aesthetic. Therefore, you should be using scale_color_brewer(...) rather than scale_fill_brewer(...).
Try adjusting the linewidth of the lines and the size of the points.
Problem 9: Stacked area
Now try to produce a stacked area chart.
Hint: include an aesthetic mapping of group = activity, like with the lines. Pay careful attention to fill vs. color. Are these areas or lines?
Problem 10: Highlight feed and rest in the line chart
One of the suggestions in the article is simply to include less data in the plot. For example, if all we want to visualize is a trade-off between feeding and resting, then all the other categories are just a distraction from the main message. We can obtain subsets of data by using the dplyr function filter().
We will explore filter() in more detail later in the course, but here’s the template:
filter(<data>, <conditions on which to filter>)
The conditions have to be logical—that is, they must evaluate to TRUE or FALSE. We test, one at a time, whether activity is equal to “Feed” OR whether activity is equal to “Rest”. In R, the symbol | means “or”. If either condition is true (condition1 | condition2), then the row is retained in the filtered data subset.
Let’s first apply the filter:
And now create the plot using the new data set called feed_rest.
ggplot(feed_rest, aes(x = month_of, y = prop, color = activity, group = activity)) +geom_line(linewidth =1.5) +geom_point(size =3) +scale_color_brewer(palette ="Set1") +labs(x ="Month", y ="Proportion of Samples")
Problem 11: Use 100% stacked bars, but only distinguish Feed
One way to accomplish this is by plotting two sets of bars:
First, the original set of all activities, but all have the same gray fill (and no color)
A filtered subset of Feed data only on top of the other bars with fill set to light blue
Let’s say that we want to plot the birth dates of all distinct baboon in the data set to look for any patterns. We can get all distinct combinations of baboons and birth dates (the variables sname and birth, respectively) using the dplyr function distinct(). The template is similar to other dplyr functions that you have seen:
As a first try, you might try using a bar chart to show this data:
ggplot(distinct_baboons, aes(x = sname, y = birth)) +geom_col() +labs(x ="Baboon ID", y ="Year of Birth")
Wow, what a mess. This is a terrible use of a bar chart. The common baseline, which starts at 1970, is completelty meaningless. The youngest animals have the longest bars! The x-axis labels are all squished together. Also, it would be easier to understand the plot if the baboons were in order of birth.
Problem 12: Birth plot using dots
Make this easier to read by using points rather than bars, flipping the x and y axis, and reordering the baboons by date of birth (hint: fct_reorder the category sname by the variable birth).
Problem 13: Heatmap
Finally, let’s make a heat map. As you saw in the chapter, heatmaps map amounts onto colors rather than x and y positions. They’re made by mapping one category to x, another category to y, and a numeric amount to fill. Then, the tiles are made by using the geom_tile() function.
Here’s a really basic version:
ggplot(b_acts_prop, aes(x = month_of, y = activity, fill = prop)) +geom_tile()
Some tips to improve appearance:
In geom_tile(), set color = "white", linewidth = 0.5 to give the tiles thin white borders.
You can force the tiles to be perfect squares by using coord_fixed(ratio = 1)
A nice continuous color gradient is scale_fill_viridis_c(). Try the various options (magma, inferno, plasma, viridis, and cividis)! For example, scale_fill_viridis_c(option = "inferno").
